Datadog | The Monitor blog

Best practices for end-to-end custom metrics governance

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Monitor OpenTelemetry-native metrics with Datadog

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Introducing RUM without Limits™: Capture everything, keep what matters

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Highlights from Google Cloud Next 2025

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Detect hallucinations in your RAG LLM applications with Datadog LLM Observability

This article explores using Large Language Models (LLMs) themselves to detect "hallucinations" – factually incorrect statements – made by other LLMs. It details how effective hallu…

Build Vega-Lite visualizations natively in Datadog with the Wildcard widget

Datadog's Terraform provider v4.0.0 introduces significant improvements including support for managing monitors as code, enhanced resource import capabilities, and streamlined auth…

Discover powerful insights with nested metric queries

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Take enhanced control of your log data with Datadog Log Workspaces

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Understand and manage your Datadog spend with Datadog cost data in Cloud Cost Management

This article details how to effectively monitor Karpenter, an open-source Kubernetes autoscaler, using Datadog. It explains how to leverage Datadog's custom metrics and auto-discov…

Simplifying the shared responsibility model: How to meet your cloud security obligations

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Introducing the Datadog Developer Hub

Datadog has launched the Developer Hub, a centralized resource for developers looking to extend the platform's capabilities. This hub provides a searchable catalog of official and …

Optimize cross-platform mobile apps with Datadog RUM and Kotlin Multiplatform support

This article discusses a new approach to debugging and observability by unifying frontend and backend data through retention filters. By strategically retaining specific data point…